1. Field of the Invention
The present invention relates to an imaging device. More particularly, the present invention relates to an apparatus and method for adjusting a white balance of an imaging device.
2. Description of the Related Art
Every light has its own color temperature value. A color temperature is a temperature representing the color of light emitted from a light source, and is generally expressed in Kelvin temperature measurement. For example, light rays of the sun, candlelight, and fluorescent lamps have different color temperature values. The reason why people hardly feel the differences between color temperatures is that human eyes have an excellent Auto White Balance function. The white balance represents relative intensities of red, green and blue colors of light emitted from a light source.
With respect to a camera, a low color temperature causes a captured image to be reddish, and a high color temperature causes a captured image to be bluish. The camera can obtain a normal color balance under sunlight. However, an image captured by the camera becomes reddish under a glow lamp or candle light, which has a lower color temperature than sunlight, and an image captured by the camera becomes bluish under a fluorescent lamp or in cloudy weather, which has a higher color temperature than sunlight. For this reason, a camera obtains a yellowish picture under a glow lamp, and obtains a bluish picture under a fluorescent lamp.
For example, adjusting a white balance may be regarded as a task of recording colors that a camera senses and receives through light on the actual spot in order to exactly express colors reflected by a subject. Generally, in order to settle discord of hues due to color temperatures, which are different depending on illumination states indoors or outdoors, an image pickup device in a camcorder or camera typically adjusts a white balance to obtain a visually smooth image in such a manner as to normally photograph a white-color chart or paper placed at a position of a subject and to balance hues in a bright portion of a screen. A white-color object is seen as a slightly different color depending on illuminations on the actual spots. Therefore, adjusting a white balance is a process of memorization that a color currently reflected by a white-color object corresponds originally to a white color in a camera.
Generally, a there is a known Von Kries method based on Gray World Assumption (GWA) is the most widely used for the white balance. The GWA is a theory that if all colors in the world are mixed an achromatic color is obtained, so that if various colors exist in an image, the mean value of all colors in the image results in an achromatic color. The achromatic color has only brightness, has no color component, and represents that the mean values of the respective RGB (Red, Green, Blue) channels are equal to each other. Adjusting the mean values obtained in the respective channels to be equal to each other based on the GWA is known as the Von Kries method.
FIG. 1 is a flowchart illustrating an entire operation of a conventional Auto White Balance algorithm for a digital image.
In step 101, pixel values of the entire region/area of an original image photographed by a digital imaging device are detected. Then, in step 103, the mean value of the detected pixel values is calculated, and in step 105, the hue components of the calculated mean value are compared do determine whether the hue component is equal to each other. When it is determined at step 105 that the color components of the calculated mean value are not equal to each other, then step 107 is performed. In step 107, color component values according to pixels are adjusted to be equal to each other, and then step 109 is performed. In contrast, at step 105, when the color components of the calculated mean value are equal to each other, the method jumps to step 109 and performs the storage step. More particularly, in step 109, the resultant image, to which an equal mean value is applied, is stored, and then the procedure is finished.
Methods which are basically enhancements of the conventional Auto White Balance algorithm and are now widely used in an image signal processors (ISPs) include: a Fuzzy Rule method (FRM) of partitioning one image into a plurality of regions, comparing the mean values of the respective regions with each other, and determining a weight value for white balance; a method of detecting an achromatic color and finding out a weight value for white balance based on the detected image; and a method of detecting an achromatic color by selecting, as achromatic colors, pixels where a brightness value is equal to or greater than a predetermined threshold value, and also an absolute value of a difference between R and G values and an absolute value of a difference between B and G values are equal to or less than a predetermined threshold value.
However, the above technologies have many problems. For example, the FRM has a problem in that the result is changed depending on the sizes of partitioned regions and the determined weight value, and the method using detection of an achromatic color has a problem in that the result is changed according to achromatic color detection methods. Even when various colors are not included in an image, as well as when an image is constituted by only one color or a few colors, there is still a degree of difficulty in estimating an accurate white point.